Inductive Databases and Constraint-Based Data Mining

By: SaÅ¡o DÅ¾eroski (Editor), Bart Goethals (Editor), PanÄ�e Panov (Editor)

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Hardcover | 2 November 2010

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476 Pages

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This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become "?rst-class citizens" and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.

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Non-Fiction Computing & I.T.Databases Data Mining Science Biology, Life Sciences Life Sciences in General Computer Science Artificial Intelligence Expert Systems & Knowledge-Based Systems

Introduction
Inductive Databases and Constraint-based Data Mining: Introduction and Overview	p. 3
Inductive Databases	p. 3
Constraint-based Data Mining	p. 7
Types of Constraints	p. 9
Functions Used in Constraints	p. 12
KDD Scenarios	p. 14
A Brief Review of Literature Resources	p. 15
The IQ (Inductive Queries for Mining Patterns and Models) Project	p. 17
What's in this Book	p. 22
Representing Entities in the OntoDM Data Mining Ontology	p. 27
Introduction	p. 27
Design Principles for the OntoDM ontology	p. 29
OntoDM Structure and Implementation	p. 33
Identification of Data Mining Entities	p. 38
Representing Data Mining Enitities in OntoDM
Related Work	p. 52
Conclusion	p. 54
A Practical Comparative Study Of Data Mining Query Languages	p. 59
Introduction	p. 60
Data Mining Tasks	p. 61
Comparison of Data Mining Query Languages	p. 62
Summary of the Results	p. 74
Conclusions	p. 76
A Theory of Inductive Query Answering	p. 79
Introduction	p. 80
Boolean Inductive Queries	p. 81
Generalized Version Spaces	p. 88
Query Decomposition	p. 90
Normal Forms	p. 98
Conclusions	p. 100
Constraint-based Mining: Selected Techniques
Generalizing Itemset Mining in a Constraint Programming Setting	p. 107
Introduction	p. 107
General Concepts	p. 109
Specialized Approaches	p. 111
A Generalized Algorithm	p. 114
A Dedicated Solver	p. 116
Using Constraint Programming Systems	p. 120
Conclusions	p. 124
From Local Patterns to Classification Models	p. 127
Introduction	p. 127
Preliminaries	p. 131
Correlated Patterns	p. 132
Finding Pattern Sets	p. 137
Direct Predictions from Patterns	p. 142
Integrated Pattern Mining	p. 146
Conclusions	p. 152
Constrained Predictive Clustering	p. 155
Introduction	p. 155
Predictive Clustering Trees	p. 156
Constrained Predictive Clustering Trees and Constraint Types	p. 161
A Search Space of (Predictive) Clustering Trees	p. 165
Algorithms for Enforcing Constraints	p. 167
Conclusion	p. 173
Finding Segmentations of Sequences	p. 177
Introduction	p. 177
Efficient Algorithms for Segmentation	p. 182
Dimensionality Reduction	p. 183
Recurrent Models	p. 185
Unimodal Segmentation	p. 188
Rearranging the Input Data Points	p. 189
Aggregate Segmentation	p. 190
Evaluating the Quality of a Segmentation: Randomization	p. 191
Model Selection by BIC and Cross-validation	p. 193
Bursty Sequences	p. 193
Conclusion	p. 194
Mining Constrained Cross-Graph Cliques in Dynamic Networks	p. 199
Introduction	p. 199
Problem Setting	p. 201
DATA-PEELER	p. 205
Extracting ¿-Contiguous Closed 3-Sets	p. 208
Constraining the Enumeration to Extract 3-Cliques	p. 212
Experimental Results	p. 217
Related Work	p. 224
Conclusion	p. 226
Probabilistic Inductive Querying Using ProbLog	p. 229
Introduction	p. 229
ProbLog: Probabilistic Prolog	p. 233
Probabilistic Inference	p. 234
Implementation	p. 238
Probabilistic Explanation Based Learning	p. 243
Local Pattern Mining	p. 245
Theory Compression	p. 249
Parameter Estimation	p. 252
Application	p. 255
Related Work in Statistical Relational Learning	p. 258
Conclusions	p. 259
Inductive Databases: Integration Approaches
Inductive Querying with Virtual Mining Views	p. 265
Introduction	p. 266
The Mining Views Framework	p. 267
An Illustrative Scenario	p. 277
Conclusions and Future Work	p. 285
SINDBAD and SiQL: Overview, Applications and Future Developments	p. 289
Introduction	p. 289
SiQL	p. 291
Example Applications	p. 296
A Web Service Interface for SINDBAD	p. 303
Future Developments	p. 305
Conclusion	p. 307
Patterns on Queries	p. 311
Introduction	p. 311
Preliminaries	p. 313
Frequent Item Set Mining	p. 319
Transforming KRIMP	p. 323
Comparing the two Approaches	p. 331
Conclusions and Prospects for Further Research	p. 333
Experiment Databases	p. 335
Introduction	p. 336
Motivation	p. 337
Related Work	p. 341
A Pilot Experiment Database	p. 343
Learning from the Past	p. 350
Conclusions	p. 358
Applications
Predicting Gene Function using Predictive Clustering Trees	p. 365
Introduction	p. 366
Related Work	p. 367
Predictive Clustering Tree Approaches for HMC	p. 369
Evaluation Measure	p. 374
Datasets	p. 375
Comparison of Clus-HMC/SC/HSC	p. 378
Comparison of (Ensembles of) CLUS-HMC to State-of-the-art Methods	p. 380
Conclusions	p. 384
Analyzing Gene Expression Data with Predictive Clustering Trees	p. 389
Introduction	p. 389
Datasets	p. 391
Predicting Multiple Clinical Parameters	p. 392
Evaluating Gene Importance with Ensembles of PCTs	p. 394
Constrained Clustering of Gene Expression Data	p. 397
Clustering gene expression time series data	p. 400
Conclusions	p. 403
Using a Solver Over the String Pattern Domain to Analyze Gene Promoter Sequences	p. 407
Introduction	p. 407
A Promoter Sequence Analysis Scenario	p. 409
The Marguerite Solver	p. 412
Tuning the Extraction Parameters	p. 413
An Objective Interestingness Measure	p. 415
Execution of the Scenario	p. 418
Conclusion	p. 422
Inductive Queries for a Drug Designing Robot Scientist	p. 425
Introduction	p. 425
The Robot Scientist Eve	p. 427
Representations of Molecular Data	p. 430
Selecting Compounds for a Drug Screening Library	p. 444
Active learning	p. 446
Conclusions	p. 448
Appendix	p. 452
Author index	p. 455
Table of Contents provided by Ingram. All Rights Reserved.

Inductive Databases and Constraint-Based Data Mining

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